Chemical shift encoded water-fat separation using parallel imaging and compressed sensing.

Chemical shift encoded techniques have received considerable attention recently because they can reliably separate water and fat in the presence of off-resonance. The insensitivity to off-resonance requires that data be acquired at multiple echo times, which increases the scan time as compared to a single echo acquisition. The increased scan time often requires that a compromise be made between the spatial resolution, the volume coverage, and the tolerance to artifacts from subject motion. This work describes a combined parallel imaging and compressed sensing approach for accelerated water-fat separation. In addition, the use of multiscale cubic B-splines for B(0) field map estimation is introduced. The water and fat images and the B(0) field map are estimated via an alternating minimization. Coil sensitivity information is derived from a calculated k-space convolution kernel and l(1)-regularization is imposed on the coil-combined water and fat image estimates. Uniform water-fat separation is demonstrated from retrospectively undersampled data in the liver, brachial plexus, ankle, and knee as well as from a prospectively undersampled acquisition of the knee at 8.6x acceleration.